Project 7 : Communicate Data Findings¶
Introduction:¶
Coronavirus disease (COVID-19) is an PANDEMIC infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2).The coronavirus COVID-19 hass affected 212 countries and territories around the world. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019. COVID-19 is a pandemic affecting many countries globally.The time between exposure to COVID-19 and the moment when symptoms start is commonly around five to six days but can range from 1 – 14 days. I have gathered Covid-19 data from different sources. Dataset contains confirmed case, deaths and recovered cases, new active cases in world and in India and indian State/UnionTerritory separately.
- Coronavirus Analysis based on Confirmed, Dead and Recovered Patients all over World.
- Second Analysis based on Deaths & Recovered Patient in India and its States and Union territories.
- Analysis based on State Testing.
- Analysis based on Time series.
Covid-19 data gathered from different sources:¶
1. On the basis of all countries: data scraped using beautiful soup https://www.worldometers.info/coronavirus/
2. Covid analysis of India:'covid_19_india.csv'
3. Analysis based on Age:'AgeGroupDetails.csv'
4. Analysis based on ICMR Testing labs:'ICMRTestingDetails.csv'
5. Analysis based on testing done in indian states:'StatewiseTestingDetails.csv'
Source : https://www.kaggle.com/sudalairajkumar/covid19-in-india
6. Analysis based on Time Series Confirmed cases all over world from :
7. Analysis based on Time Series Recovered case all over world:'time_series_covid19_recovered.csv'.
8. Analysis based on Time Series Deaths case all over world:'time_series_covid19_deaths.csv'
Source : https://data.humdata.org/dataset/5dff64bc-a671-48da-aa87-2ca40d7abf02Covid_19 All countries scraped using beautiful soup from worldometers.com:¶
* Dataset coloums:
- Total_cases
- Total_deaths
- Total_Recovered
- New_cases
- New Deaths
- Active_Cases
- Serious_Case
- TotCases/1Mpop
- Deaths/1Mpop
- Total Tests
- Test/1Mpopfull_data.head(10)
Covid_19 India CSV file :¶
- Confirmed cases, Deaths, Cured cases. -Date: 30/01/2 till 06/05/20
covid_india.head(5)
30/jan/2020 india reported first case in kerala
02/march/2020 india reported their covid_19 cases i Telengana ,Delhi and after that covid_19 cases reported to in other state and /UnionTerritory.
13/03/2020 Karnataka reported death of one patients due to covid_19
Based on Agegroup Covid_19 Cases in India¶
covid_india_age = pd.read_csv('AgeGroupDetails.csv')
covid_india_age
covid_india_testing = pd.read_csv('ICMRTestingDetails.csv')
covid_india_testing.tail(5)
Covid_19 India testing CSV file :¶
- CSV contain Statewise Testing Positive and Negative Patients data.
covid_india_state_testing = pd.read_csv('StatewiseTestingDetails.csv')
covid_india_state_testing
Time Series Covid_19 case Csv file :¶
-TimeSeries data contain DATE and Country information.
covid_time_series_C= pd.read_csv('time_series_covid19_confirmed.csv')
covid_time_series_C
covid_time_series_covid_19_R = pd.read_csv('time_series_covid19_recovered.csv')
covid_time_series_covid_19_R
covid_time_series_D = pd.read_csv('time_series_covid19_deaths.csv')
covid_time_series_D
Assessing Data:¶
**Assessing Full_data of covid_19 all countries Data
full_data.info()
At the time of writing this report.
Total 216 countries, 177 countries reported deaths.
TotalRecovered cases reported in 207 countries.
82 NewDeaths are reported.
Testing reported in 177 countries.
** Some data is missing. It may be True or confirmed cases, deaths,Recovered cases are not reported. We can not analyse such cases.
Data Issue 1:¶
***Rename Columns name in full_data dataframe .
full_data.fillna(0, inplace=True)
full_data['new_cases'] = full_data['new_cases'].str.replace(',', '', regex=True)
full_data['new_deaths'] = full_data['new_deaths'].str.replace(',','', regex=True)
full_data['new_cases'].fillna(0,inplace=True)
full_data['new_deaths'].fillna(0,inplace=True)
full_data['new_cases'] = full_data['new_cases'].astype('int64')
full_data['new_deaths'] = full_data['new_deaths'].astype('int64')
full_data
full_data.describe()
full_data.sum()
countries = pd.read_csv('countries_data.csv', encoding= 'unicode_escape')
countries
covid_india.info()
covid_india.describe()
covid_india_state_testing.describe()
covid_time_series_C.describe()
covid_time_series_covid_19_R.describe()
covid_time_series_D.describe()
Data Issue 5:¶
***Drop [Sno.] from column in Covid_india_age dataframe.
covid_india_age
Data Issue 6:¶
*** Missing value (NaN)in 3 covid_time_series dataframe:Confirmed,Recovered,Death.
Data Issue 7:¶
***Drop [ lat,long] from column in Covid time_series dataframe. irrelevent in this analysis
#covid_time_series_covid_19_R = covid_time_series_covid_19_R.drop(['Province/State','Lat','Long'], axis=1)
covid_time_series_covid_19_R = covid_time_series_covid_19_R.drop(['Lat','Long'], axis=1)
covid_time_series_covid_19_R
Data Issue 8:¶
** Difficult to analyze time_series data.
Time series data consist of day wise date in different coloums which is not good for analysis. First we Unpivot date columns[3:] with variable column ‘Date’ and value column ‘Confirmed’, 'Recovered', 'Death'.
dates = covid_time_series_C.columns[3:]
covid_time_series_C = covid_time_series_C.melt(
id_vars=['Country/Region','Province/State'],
value_vars=dates,
var_name='Date',
value_name='Confirmed'
)
covid_time_series_C
dates = covid_time_series_D.columns[3:]
covid_time_series_D = covid_time_series_D.melt(
id_vars=['Country/Region','Province/State'],
value_vars=dates,
var_name='Date',
value_name='Deaths'
)
covid_time_series_D = covid_time_series_D.groupby(['Country/Region', 'Date'], as_index=False)['Deaths'].sum()
covid_time_series_D
dates = covid_time_series_covid_19_R.columns[3:]
covid_time_series_covid_19_R= covid_time_series_covid_19_R.melt(
id_vars=['Country/Region','Province/State'],
value_vars=dates,
var_name='Date',
value_name='Recovered'
)
Data Issue 9¶
Also world data consist on provincial data from different countrys. Country data is grouped and aggregated using group by.
Result of group by is a series object with country data grouped by date. I have converted the series object into dataframe to avoid grouping.
covid_time_series_covid_19_R = covid_time_series_covid_19_R.groupby(['Country/Region', 'Date'], as_index=False)['Recovered'].sum()
covid_time_series_covid_19_R
Data Issue 10:¶
*Merge
Covid_time_series_C, Covid_time_series_D, Covid_time_series_covid_19_R
using merge function.
*Calculate Death_percentage and Recovered_percentage in Covid_time_series dataframe.
Data Issue 11:¶
As the cases started appearing in countries at different time. This way on a particualr date it isnot a good comparision between difeerent countries as we ought to analyse the outbread tread rate. I calclated the first date of corono positive case in each country and added a column in the dataframe. Then using this date I assigned the days passed after 1st corona case to each row. Now we can compare different couries based one days past first case.
#covid_time_seriesChina = covid_time_series[covid_time_series['Country/Region'] == 'China']
#covid_time_seriesChina.head(50)
covid_time_series
Data Issue 12:¶
*** Replace NaN value with Zero in Death_Percentage ,Recovered_percentage in covid_time_series.
covid_time_series_I = covid_time_series[covid_time_series['Country/Region']=='India']
covid_time_series_I.head(5)
fig = px.scatter(full_data,x="total_cases",y="total_deaths",color='country',log_x=True ,log_y=True ,size_max=100, range_x=[1,1000000000],range_y=[1,1000000])
fig.update_traces(textposition='top center')
fig.update_layout(
# height=800,width=1000,
title_text='Total Deaths Cases in the world',xaxis = dict(
tickangle = 90,
title_text = "Total_cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Deaths Cases",
title_standoff = 10)
)
fig.show()
-***This plot shows country wise death cases vs total confirmend on a logarithmic scale.
- Total Deaths Reported in many countries below 10000
As we can see in the graph top five countries on the bases of total confirmed cases, total_deaths rate, recovered cases are
- USA,
- Spain ,
- Italy,
- UK,
- Russia
fig = px.scatter(full_data,x="total_cases",y="total_recovered",color='country', log_x=True ,log_y=True ,size_max=100, range_x=[1,10000000],range_y=[1,1000000])
fig.update_traces(textposition='top center')
fig.update_layout(
# height=800,width=1000,
title_text='Total Recovered Cases in the world',
xaxis = dict(
tickangle = 90,
title_text = "Total_cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_recovered Cases",
title_standoff = 10)
)
fig.show()
* The following plot shows country wise recoverd cases on a logarithmic scale. * here all countries is consider for analysis so, there is overlapping in scatter plot.
Analysis of Covid_19 Cases in all countries with Statsmodel:¶
-Statsmodels module was used for covid_19 all country data analysis that provides classes and functions for the estimation regression models, for conducting statistical tests, and statistical data exploration of covid_19 data ('total_cases' ,'total_deaths' ,'new_cases' ,'new_deaths' ,'total_recovered' ,'active_cases') in all countries.
Assumptions of a regression model:
- Relevance of data to the covid_19 cases.
- Linearity and additivity
- Independence of residuals
- Constancy of variance of residuals
- Normal distribution of residuals
X = full_data[['total_deaths','new_cases','new_deaths']]
# #### fit a OLS model with intercept on total_cases and new_cases,new_deaths.
y = full_data['total_cases']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())
Regression Analysis:¶
*β₀ and β₁ are chosen to minimize the square of the distance between the predicted values and the actual values.
Adj. R-squared indicates that 99% of total_deaths,new_cases,new_deaths can be explained by our predictor variable.¶
In order to understand trends we look at the slope of the death cases, new cases and total deaths in linearscale..
From our results, we see that • The intercept 𝛽̂0 = -111.27
The regression coefficient (coef) represents the change in the dependent variable resulting from a one unit change in the predictor variable, all other variables being held constant. In our model, a one unit increase in total_deaths, new_cases,new_deaths increase total_cases.
• The slope 𝛽̂1 = 5.0908
• The slope 𝛽̂2 = 11.7764 .
• The slope 𝛽̂3 = 321.7351
• The positive 𝛽̂3 parameter estimate implies high new_deaths rate In line with our assumptions, an increase in total_deaths, new_cases, new_deaths appears to increase the total cases.
The p-value means the probability of an 5.0908 increase in total_cases due to a one unit increase in total_deaths is 0%, assuming there is no relationship between the two variables.
• The p-value of total_deaths and new_cases,new_deaths 0.000 for 𝛽̂1implies that is statistically significant (using p < 0.05 as a rejection rule).¶
• The p-value of new_deaths is 0.03¶
** The standard error measures the accuracy of total_deaths coefficient by estimating the variation of the coefficient if the same test were run on a different sample . Our standard error 1, is low and therefore appears accurate.
• The R-squared value of R-squared:0.994.¶
fig = plt.figure(figsize =(15,8))
results = smf.ols('total_cases~total_deaths+new_cases+new_deaths',data = full_data).fit()
sm.graphics.plot_regress_exog(results, 'total_deaths', fig=fig)
plt.show()
Regression Plot Analysis:¶
- The “Y and Fitted vs. X” graph plots the dependent variable against our predicted values with a confidence interval. The inverse relationship in our graph indicates that total_cases and total_deaths are positively correlated, i.e., when one variable increases the other increase.
2.The “Residuals versus total_deaths graph shows our model's errors versus the specified predictor variable. Each dot is an observed value; the line represents the mean of those observed values.
3.The “Partial regression plot” shows the relationship between total_cases and total_deaths,the impact of adding other independent variables on our existing total_deaths coefficient.
4.the Component and Component Plus Residual (CCPR) plot is an extension of the partial regression plot, but shows where our trend line would lie after adding the impact of adding our other independent variables on our existing total_deaths coefficient.This is the "component" part of the plot and is intended to show where the "fitted line" would lie.
X = full_data[['total_recovered','new_cases','active_cases']]
#### fit a OLS model with intercept on total_recovered and new_cases,active_cases.
y = full_data['total_cases']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())
Regression Analysis:¶
*β₀ and β₁ are chosen to minimize the square of the distance between the predicted values and the actual values.
Adj. R-squared indicates that 100% of total_recovered,new_cases,active_cases can be explained by our predictor variable.¶
In order to understand trends we look at the slope of the recoverd_cases, new cases and active_case in linearscale..
From our results, we see that • The intercept 𝛽̂0 = 3.0684 The regression coefficient (coef) represents the change in the dependent variable resulting from a one unit change in the predictor variable, all other variables being held constant. In our model, a one unit increase the recoverd_cases, new cases and active_case increase total_cases.
• The slope 𝛽̂1 = 1.0937.
• The slope 𝛽̂2 = 0.1470
• The slope 𝛽̂3 = 1.0588.
• The positive 𝛽̂2 parameter estimate implies low new_cases rate In line with our assumptions, an increase in the recoverd_cases,active_case appears to increase the total cases.
The p-value means the probability of an 1.1121 increase in total_cases due to a one unit increase in total_recovered is 0%, assuming there is no relationship between the two variables.
• The p-value of total_deaths and new_cases,new_deaths 0.000 for 𝛽̂1implies that is statistically significant (using p < 0.05 as a rejection rule).¶
** The standard error measures the accuracy of total_deaths coefficient by estimating the variation of the coefficient if the same test were run on a different sample . Our standard error, 0.007, is low and therefore appears accurate.
• The R-squared value of R-squared:1.¶
fig = plt.figure(figsize =(15,8))
results = smf.ols('total_cases~total_recovered + new_cases+active_cases', data = full_data).fit()
#sm.graphics.plot_ccpr_grid(results, fig=fig)
sm.graphics.plot_regress_exog(results, 'total_recovered', fig=fig)
plt.show()
###endogenous: caused by factors within the system ,exogenous: caused by factors outside the system
Regression plot Analysis:¶
- The “Y and Fitted vs. X” graph plots the dependent variable against our predicted values with a confidence interval. The inverse relationship in our graph indicates that total_cases and total_recovered cases are positively correlated, i.e., when one variable increases the other increase.
2.The “Residuals versus total_deaths graph shows our model's errors versus the specified predictor variable. Each dot is an observed value; the line represents the mean of those observed values.
3.The “Partial regression plot” shows the relationship between total_cases and total_recovered,the impact of adding other independent variables on our existing total_recovered coefficient.
4.the Component and Component Plus Residual (CCPR) plot is an extension of the partial regression plot, but shows where our trend line would lie after adding the impact of adding our other independent variables on our existing total_recovered coefficient.This is the "component" part of the plot and is intended to show where the "fitted line" would lie.
Analysis of Covid_19 Total_cases ,Active_Case, Serious Cases in All countries.¶
fig = px.scatter(full_data, y='total_cases', x='active_cases',animation_frame="active_cases",text = "country",range_x =[0,100000],range_y=[0,100000])
fig.update_layout(
# height=800,width=1000,
title_text='Total Active_Cases in All countries',xaxis = dict(
tickangle = 90,
title_text = "Active_Cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Cases",
title_standoff = 10)
)
fig.show()
fig2 = px.scatter(full_data, y='total_cases', x='serious', animation_frame="serious",text ='country',range_x =[0,10000],range_y=[0,100000])
fig2.update_layout(
#height=800,width=1000,
title_text='Total Serious Cases in the world',xaxis = dict(
tickangle = 90,
title_text = "Serious_cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Cases",
title_standoff = 10)
)
fig2.show()
Analysis of Covid_19 Totcase/1Mpop,Deaths/1Mpop,Tests/1Mpop in All countries.¶
fig = px.scatter(full_data, x='tot\xa0cases/1m_pop', y='deaths/1m_pop', color='country',log_x=True ,log_y=True ,size_max=45)
fig.update_layout(
# height=800,width=1000,
title_text='Total Deaths Cases/ 1m_pop in the world',xaxis = dict(
tickangle = 90,
title_text = "Total_cases/1m_pop",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Deaths Cases/1m_pop",
title_standoff = 10)
)
fig.show()
fig2 = px.scatter(full_data, x='tot\xa0cases/1m_pop', y='tests/_1m_pop', color='country',log_x=True ,log_y=True ,size_max=45)
fig2.update_layout(
#height=800,width=1000,
title_text='Total Test/1m_pop in the world',xaxis = dict(
tickangle = 90,
title_text = "Total_cases/1m_pop",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Test/1m_pop",
title_standoff = 10)
)
fig2.show()
World Maps of Total_covid 19 Cases in Countries:¶
- Total cases Maps using Plotly ,Mapbox and animation Show how the covid19 Spread in countries.
full_data.fillna(0, inplace=True)
full_data.head(10)
fig = px.scatter_mapbox(full_data, lat="lat", lon="long",color = 'country', hover_name="country", hover_data=['total_cases', "total_deaths"],
color_continuous_scale=px.colors.cyclical.IceFire,
animation_frame='total_cases',size_max=55, zoom=3)
fig.update_layout(title_text="Total_Cases in World")
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
World Maps of Total_Deaths Cases in All Countries:¶
- Total deaths Maps using Plotly ,Mapbox .
fig = px.scatter_mapbox(full_data, lat="lat", lon="long",color = 'country', hover_name="country", hover_data=['total_cases',"total_deaths" ,"total_recovered"],
color_continuous_scale=px.colors.cyclical.IceFire,
animation_frame='total_deaths',zoom=3)
fig.update_layout(title_text="Total_Deaths in World")
fig.update_layout(
mapbox_style="white-bg",
mapbox_layers=[
{
"below": 'traces',
"sourcetype": "raster",
"source": [
"https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}"
]
}
])
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
full_data=pd.melt(full_data, id_vars=['country','tests/_1m_pop'], value_vars=['total_cases', 'new_cases', 'total_deaths', 'new_deaths', 'total_recovered'])
# plotly
fig = px.line(full_data, x='country', y='value', color='variable',log_x=False ,log_y=True)
fig.update_layout(
# height=800,width=1000,
title_text='Total Cases in the world',xaxis = dict(
tickangle = 90,
title_text = "Country",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Value",
title_standoff = 10)
)
# Show plot
fig.show()
Univariate analysis:¶
base_color = sns.color_palette()[1]
plt.figure(figsize=(32,6))
g = sns.countplot(data = covid_india, x ='State/UnionTerritory', color = base_color)
g.set_xticklabels(g.get_xticklabels(), rotation=45)
g.set_title('Covid_19 Analysis Based on State/UnionTerriory')
n_points = covid_india.shape[0]
cat_counts = covid_india['State/UnionTerritory'].value_counts()
locs, labels = plt.xticks()
for loc, label in zip(locs, labels):
count = cat_counts[label.get_text()]
pct_string = '{:0.1f}%'.format(100*count/n_points)
plt.text(loc, count-8, pct_string, ha = 'left',va='bottom', color = 'black')
BiVariate Analysis of Covid_19 Cases in India:¶
fig = px.scatter(covid_india,x="Confirmed",y="Deaths" ,animation_frame="Deaths", animation_group="State/UnionTerritory",color="State/UnionTerritory",log_x=True ,log_y=True , range_x=[1,10000],range_y=[1,10000])
fig.update_traces(textposition='top center')
fig.update_layout(
#height=800,width=1000,
title_text='Total Deaths Cases in India',xaxis = dict(
tickangle = 90,
title_text = "Total_cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total_Deaths Cases",
title_standoff = 10),
)
fig.show()
fig = px.scatter(covid_india,x="Confirmed",y="Cured", animation_frame="Cured", animation_group="State/UnionTerritory",color="State/UnionTerritory", log_x=True ,log_y=True ,size_max=45, range_x=[1,10000],range_y=[1,10000])
fig.update_traces(textposition='top center')
fig.update_layout(
#height=800,width=1000,
title_text='Total Recovered Cases in India',xaxis = dict(
tickangle = 90,
title_text = "Total_cases",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Total Recovered Cases",
title_standoff = 10)
)
fig.show()
Analysis of Covid_19 Cases in India Based on StatsModel:¶
-Statsmodels module is used for covid_19 cases that provides classes and functions for the estimation regression models,for conducting statistical tests, and statistical data exploration of covid_19('Confirmed cases','Deaths cases','Cured cases')in india.
X = covid_india[['Deaths']]
#### fit a OLS model with intercept on Deaths
y = covid_india['Confirmed']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())
Regression Analysis:¶
*β₀ and β₁ are chosen to minimize the square of the distance between the predicted values and the actual values.
Adj. R-squared indicates that 89% of deaths can be explained by our predictor variable.¶
In order to understand trends we look at the slope of the death cases in linearscale..
From our results, we see that • The intercept 𝛽̂0 = 113 The regression coefficient (coef) represents the change in the dependent variable resulting from a one unit change in the predictor variable, all other variables being held constant. In our model, a one unit increase in deaths, new_cases,increase Confirmed _cases. • The slope 𝛽̂1 = 22.66 In line with our assumptions, an increase in deaths appears to increase the confirmed cases.
The p-value means the probability of an 22.66 increase in Confirmed_cases due to a one unit increase in deaths is 0%, assuming there is no relationship between the two variables.
• The p-value of deaths is 0.000 for 𝛽̂1implies that is statistically significant (using p < 0.05 as a rejection rule).¶
** The standard error measures the accuracy of deaths coefficient by estimating the variation of the coefficient if the same test were run on a different sample . Our standard error, 0.195, is low and therefore appears accurate.
• The R-squared value of R-squared:0.89 .¶
fig = plt.figure(figsize =(15,8))
#full_data1= sm.dataset.full_data.load_pandas()
results = smf.ols('Confirmed ~Deaths ', data = covid_india).fit()
#sm.graphics.plot_ccpr_grid(results, fig=fig)
sm.graphics.plot_regress_exog(results, 'Deaths', fig=fig)
plt.show()
Regression Plot Analysis:¶
- The “Y and Fitted vs. X” graph plots the dependent variable against our predicted values with a confidence interval. The inverse relationship in our graph indicates that Confirmed_cases and deaths are positively correlated, i.e., when one variable increases the other increase.
2.The “Residuals versus total_deaths graph shows our model's errors versus the specified predictor variable. Each dot is an observed value; the line represents the mean of those observed values.
3.The “Partial regression plot” shows the relationship between Confirmed_cases and deaths,the impact of adding other independent variables on our existing deaths coefficient.
4.the Component and Component Plus Residual (CCPR) plot is an extension of the partial regression plot, but shows where our trend line would lie after adding the impact of adding our other independent variables on our existing deaths coefficient.This is the "component" part of the plot and is intended to show where the "fitted line" would lie.
X = covid_india[['Cured']]
#### fit a OLS model with intercept on Cured Cases
y = covid_india['Confirmed']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())
Regression Analysis:¶
*β₀ and β₁ are chosen to minimize the square of the distance between the predicted values and the actual values.
Adj. R-squared indicates that 79% of Cured Cases can be explained by our predictor variable.¶
In order to understand trends we look at the slope of the Cured Cases in linearscale..
From our results, we see that • The intercept 𝛽̂0 = 42.61 The regression coefficient (coef) represents the change in the dependent variable resulting from a one unit change in the predictor variable, all other variables being held constant. In our model, a one unit increase in Cured increase Confirmed. • The slope 𝛽̂1 = 4.21 In line with our assumptions, an increase in cured cases appears to increase the Confirmed cases.
The p-value means the probability of an 4.21 increase in Confirmed cases due to a one unit increase in Cured Cases is 0%, assuming there is no relationship between the two variables.
• The p-value of cured 0.000 for 𝛽̂1implies that is statistically significant (using p < 0.05 as a rejection rule).¶
** The standard error measures the accuracy of total_deaths coefficient by estimating the variation of the coefficient if the same test were run on a different sample . Our standard error, 0.050, is low and therefore appears accurate.
• The R-squared value of R-squared:0.79 .¶
fig = plt.figure(figsize =(15,8))
#full_data1= sm.dataset.full_data.load_pandas()
results = smf.ols('Confirmed ~Cured', data = covid_india).fit()
#sm.graphics.plot_ccpr_grid(results, fig=fig)
sm.graphics.plot_regress_exog(results, 'Cured', fig=fig)
plt.show()
Regression Plot Analysis:¶
- The “Y and Fitted vs. X” graph plots the dependent variable against our predicted values with a confidence interval. The inverse relationship in our graph indicates that Cured and Confirmed are positively correlated, i.e., when one variable increases the other increase.
2.The “Residuals versus cured graph shows our model's errors versus the specified predictor variable. Each dot is an observed value; the line represents the mean of those observed values.
3.The “Partial regression plot” shows the relationship between Cured and confirmed cases ,the impact of adding other independent variables on our existing cured coefficient.
4.the Component and Component Plus Residual (CCPR) plot is an extension of the partial regression plot, but shows where our trend line would lie after adding the impact of adding our other independent variables on our existing cured coefficient.This is the "component" part of the plot and is intended to show where the "fitted line" would lie.
Analysis of Covid_19 Cases in India and its State /Union Territory :¶
f,g = plt.subplots(figsize = (15,10))
base_color = sns.color_palette()[0]
g = sns.barplot(data = covid_india, x = 'Confirmed', y = 'State/UnionTerritory',
label = 'Total Confirmed Cases',color = base_color)
sns.set_color_codes('muted')
g = sns.barplot(x = 'Cured', y = 'State/UnionTerritory', data = covid_india,
label = 'Total number of Cured', color = 'R', edgecolor = 'w')
sns.set_color_codes('pastel')
g= sns.barplot(x = 'Deaths', y = 'State/UnionTerritory', data = covid_india,
label = 'Total number of Deaths', color = 'g', edgecolor = 'w')
g.set_title('Analysis of Covid_19 Cases in India')
g.legend(ncol = 3, loc = 'lower right')
#sns.despine(left = True, bottom = True)
plt.show()
Analysis of Covid_19 Cases in India and its State /Union Territory¶
plt.figure(2, figsize=(20,15))
fig,ax = plt.subplots(1, 2)
g =sns.scatterplot(data=covid_india,y="Deaths",x="Confirmed",ax=ax[0],hue="State/UnionTerritory",palette="deep")
g.legend(loc='upper right', bbox_to_anchor=(.20, 0.0), ncol=1)
g =sns.scatterplot(data=covid_india,y="Cured",x="Confirmed",ax=ax[1],hue="State/UnionTerritory",palette="deep")
g.set_title('Covid_19 Deaths Cases and Recovered Cases in India and its State/ Union Territory')
g.legend(loc='upper left', bbox_to_anchor=(1.0, 0.0), ncol=1)
Analysis Based on Age group in india¶
fig = px.bar(covid_india_age, y='TotalCases', x='AgeGroup', text='Percentage')
fig.update_traces(texttemplate='%{text}', textposition='outside',marker_color='lightsalmon')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text="Anaysis Based on Age Group in India")
fig.show()
Analysis of Covid_19 Cases Based on Testing (Positive Cases and Negative Case) in India :¶
fig = px.scatter(covid_india_state_testing, x='Date', y='Positive', title='Positive Cases Time Series with Rangeslider', range_x=['2020-01-23','2020-05-04'],range_y = [1,100000])
fig.update_traces(marker_color='indianred')
fig.update_xaxes(rangeslider_visible=True)
fig.update_layout(
# height=800,width=1000,
title_text='Covid_19 Testing in India',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Positive",
title_standoff = 10),
)
fig.show()
fig1 = px.scatter(covid_india_state_testing, x='Date', y='Negative', title='Negative Cases Time Series with Rangeslider',range_x = ['2020-01-23','2020-05-04'],range_y = [1,1000000])
fig1.update_traces(marker_color='lightsalmon')
fig1.update_xaxes(rangeslider_visible=True)
fig1.update_layout(
# height=800,width=1000,
title_text='Covid_19 Testing in India',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Negative",
title_standoff = 10),
)
fig1.show()
Covid_Time_Series Analysis: Datewise Confirmed Cases,Deaths, Recovered Cases in All Countries :¶
Date:22/jan/2020 till 6/may/2020
- The total number of confirmed cases to date for each country and province.
- The percentage of confirmed cases,Deaths,Recovered cases.
- A maximum confirmed cases in all countries.
- A maximum deaths cases in all countries.
- A maximum recovered cases in all countries
fig = px.scatter(covid_time_series_C, x='Date', y='Confirmed',color="Country/Region", title='Confirmed Cases Time Series with Rangeslider',range_x=['1/22/20','4/29/20'],range_y = [1,1500000])
#fig.update_traces(marker_color='darkslateblue')
fig.update_layout(
# height=800,width=1000,
title_text= 'Datewise Confirmed Cases in all Countries',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Confirmed Cases",
title_standoff = 10),
)
fig.update_xaxes(rangeslider_visible=True)
fig.show()
fig = px.scatter(covid_time_series_D, x='Date', y='Deaths',color="Country/Region", title='Deaths Cases Time Series with Rangeslider',range_x=['1/22/20','4/29/20'],range_y = [1,100000])
#fig.update_traces(marker_color='sandybrown')
fig.update_layout(
# height=800,width=1000,
title_text= 'Datewise Deaths Cases in all Countries',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = " Deaths Cases",
title_standoff = 10),
)
fig.update_xaxes(rangeslider_visible=True)
fig.show()
fig = px.scatter(covid_time_series_covid_19_R, x='Date', y='Recovered',color="Country/Region", title='Recovered Cases Time Series with Rangeslider',range_x=['1/22/20','4/29/20'],range_y = [1,1000000])
#fig.update_traces(marker_color='green')
fig.update_layout(
#height=800,width=1000,
title_text= 'Datewise Recovered Cases in all Countries',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Recovered Cases",
title_standoff = 10),
)
fig.update_xaxes(rangeslider_visible=True)
fig.show()
Covid_Time_Series Analysis: Datewise Confirmed Cases,Deaths, Recovered Cases in India :¶
Date:22/jan/2020 till 6/may/2020
fig1 = px.scatter(covid_time_series_I, x='Date', y='Confirmed',color="Country/Region", title='Recovered Cases Time Series with Rangeslider',range_x=['1/22/20','4/29/20'],range_y = [1,10000])
fig1.update_layout(
# height=800,width=1000,
title_text= 'Datewise Confirmed Cases in India',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Confirmed Cases",
title_standoff = 10),
)
fig1.update_xaxes(rangeslider_visible=True)
fig1.show()
fig2 = px.scatter(covid_time_series_I, x='Date', y='Deaths',color="Country/Region",range_x=['1/22/20','4/29/20'],range_y = [1,10000])
fig2.update_layout(
#height=800,width=1000,
title_text= 'Datewise Deaths Cases in India ',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Deaths Cases",
title_standoff = 10),
)
fig2.update_xaxes(rangeslider_visible=True)
fig2.show()
fig3 = px.scatter(covid_time_series_I, x='Date', y='Recovered',color="Country/Region", title='Recovered Cases Time Series with Rangeslider',range_x=['1/22/20','4/29/20'],range_y = [1,10000])
fig3.update_layout(
#height=800,width=1000,
title_text= 'Datewise Recovered Cases in India ',xaxis = dict(
tickangle = 90,
title_text = "Date",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Recovered Cases",
title_standoff = 10),
)
fig3.update_xaxes(rangeslider_visible=True)
fig3.show()
Daywise Covid_19 Analysis:¶
Countries had the highest number of confirmed cases , Deaths Cases and recovered cases in one particular day.
fig = px.scatter(covid_time_series, x='Day', y='Confirmed',color="Country/Region",log_x=False ,log_y=True )
fig.update_layout(
#height=800,width=1000,
title_text= 'Daywise Confirmed Cases in All Countries ',xaxis = dict(
tickangle = 90,
title_text = "Day",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Confirmed Cases",
title_standoff = 10),
)
fig.show()
fig1 = px.scatter(covid_time_series, x='Day', y='Deaths',color="Country/Region",log_x=False ,log_y=True )
fig1.update_layout(
#height=800,width=1000,
title_text= 'Daywise Deaths Cases in All Countries ',xaxis = dict(
tickangle = 90,
title_text = "Day",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Deaths Cases",
title_standoff = 10),
)
fig1.show()
fig2 = px.scatter(covid_time_series, x='Day', y='Recovered',color="Country/Region",log_x=False ,log_y=True )
fig2.update_layout(
#height=800,width=1000,
title_text= 'Daywise Recovered Cases in All Countries ',xaxis = dict(
tickangle = 90,
title_text = "Day",
title_font = {"size": 15},
title_standoff = 10),
yaxis = dict(
title_text = "Recovered Cases",
title_standoff = 10),
)
fig2.show()
Conclusion:¶
-Analyze curated data related to the Covid_19 Viral Disease to understand the magnitude of the risk posed by this infectious Disease in all Countries:
- First cases of covid_19 is reported in Wuhan (the city where the virus originated)Central China, with a population of over 11 million people.The city, on January 23.
- 2-14 days represents the current official estimated range for the novel coronavirus COVID-19.
- On January Month , the novel coronavirus cases in the UK,Russia,Sweden, Spain were reported less in number.
- On March and April Months there is tremendous increase of Confirmed cases and Deaths Cases in all over World.
** Total Confirmed Case in World =3,913,644
** Total Deaths Case in World = 270,426
** Total Recovered Case in World=1,341,022
** Total Confirmed Case in India = 56,342
** Total Deaths Case in India = 16,540
** Total Recovered Case in India =1,886
(* current data reported in conclusion)
Age and conditions of Coronavirus cases in india reported TotalSamples :1.57851e+07 Negative 1.30916e+07 Positive 571595
Time series data show that their increase in confirmed cases rapidly .and also the death rate increase in countries .
- Time Series data also show that recovered cases more than the death cases in countries .
- Confirmed refers to a case being reported in contrast to a case being infected. Therefore, fluctuations in days .This is due to the sampling bias induced by the limited amount of corona test kits. -So many cases that do not yet show symptoms are tested and the mortality rate below is shown in dead per million inhabitants.
def hide_code_in_slideshow():
from IPython import display
import binascii
import os
uid = binascii.hexlify(os.urandom(8)).decode()
html = """<div id="%s"></div>
<script type="text/javascript">
$(function(){
var p = $("#%s");
if (p.length==0) return;
while (!p.hasClass("cell")) {
p=p.parent();
if (p.prop("tagName") =="body") return;
}
var cell = p;
cell.find(".input").addClass("hide-in-slideshow")
});
</script>""" % (uid, uid)
display.display_html(html, raw=True)
hide_code_in_slideshow()